In the previous blog post Data analytics with Jaeger aka traces tell us more! we have introduced our data science initiative and platform. The ultimate goal is to develop new functionality within the Jaeger project based on AI/ML that will provide new insights into our applications. This type of functionality is also referred to as AI operations (AIOps). Jupyter notebooks provide a simple user interface for experimenting with data.

Weave trimmed troubleshooting fat, cut API response time from seconds to milliseconds with Jaeger.

In this article we will look at the agent-like instrumentation tool T-Trace. The tool provides non-intrusive instrumentation capabilities for applications running on GraalVM. We will instrument a simple NodeJS application by using T-Trace and OpenTracing API with Jaeger NodeJS tracer.

Software monitoring allows developers and IT professionals to observe events occurring within a monitored system. The data gathered by monitoring processes offers visibility into how the monitored entity is behaving and provides warning signs indicating that some aspect of the system deserves greater attention. More and more software is migrating to the cloud, and monolithic software is being decomposed into microservices to create distributed applications.

Meet Stackdriver Logging, a gregarious individual who loves large-scale data and is openly friendly to structured and unstructured data alike. Although they grew up at Google, Stackdriver Logging welcomes data from any cloud or even on-prem. Logging has many close friends, including Monitoring, BigQuery, Pub/Sub, Cloud Storage and all the other Google Cloud services that integrate with them. However, recently, they are looking for a deeper relationship to find insight.

Since AWS Lambda was launched in 2014, serverless has transformed the way applications are built, deployed, and managed. By abstracting away the underlying infrastructure, developers are able to shift operational responsibilities to the cloud provider and focus on solving customer problems.

I will get straight to the point, Jaeger at the moment only visualizes collected data from instrumented applications. It does not perform any post-processing (except service dependency diagram) or any calculations to derive other interesting metrics or features from traces it collects. This is a pity because traces contain the richest information from all telemetry signals combined!

In this article, we are going to look at deploying Jaeger on Kubernetes and OpenShift with Elasticsearch storage using operators. We will go through the configuration (custom resources) and also see what benefits OpenShift offers over vanilla Kubernetes.

Teams switching from a monolithic application architecture to microservices often face a jarring realization: their time-tested troubleshooting techniques don’t work as effectively. A microservice consists of many independent, distributed, and ephemeral services with varying capabilities for monitoring and logging. Techniques such as stack traces are effective troubleshooting tools in monoliths, but only paint a small portion of the big picture in a microservice-based application.

(This is the first post by our new head of Customer Success, Irving.) Sampling is a must for applications at scale; it’s a technique for reducing the burden on your infrastructure and telemetry systems by only keeping data on a statistical sample of requests rather than 100% of requests. Large systems may produce large volumes of similar requests which can be de-duplicated.